In the domain of image processing, the photometric stereo method is a well-known method for producing a raised relief map of the surface of an object. The photometric stereo method operates by using a plurality of images of an object taken with a camera in a fixed position, with one or more illumination sources providing different directions of illumination. The aim of the photometric stereo method is to provide a raised relief map of the surface of an object by applying a specific algorithm to data related to images being taken. In the domain of biological analysis, for example, the objects comprise micro-organisms. The raised relief maps of objects such as micro-organisms obtained using such a method allow several characteristics of the micro-organisms to be identified, such as the texture of the surface and the type of relief. Thus, the algorithm generates a raised relief map or a deepness map also called depth map of the surface of the object.
In addition, in the specific domain of biological analysis, in vitro analysis of a biological sample is performed to determine the type of micro-organism potentially present in the biological sample. A specific environment is associated with the biological sample to allow the growth of micro-organisms. The specific environment can be located, for example, in a Petri dish. After incubation of a Petri dish comprising a biological sample, the surface of the Petri dish may be illuminated with an imaging system as described in published European application EP2520923. The imaging system comprises several illumination sources located around the biological sample. Each illumination source may illuminate the biological sample from a different direction. A fixed position camera may take different images of a same surface of the Petri dish. The plurality of illumination sources associated with different directions of illumination represents complex illumination conditions.
In the prior art, a photometric stereo method as disclosed in bibliographical reference [12] comprises an algorithm which takes into account three matrices related to three components describing image properties of grey scale images when illumination occurs. A first matrix A relates to albedo values which represent the ratio of incident illumination being reflected by the surface of the object. The matrix A comprises the same value for all the pixels as the entire surface is, presumably, made of the same material. A second matrix V represents the location of the illumination source for each image captured by the camera. A third matrix N represents the normal value of each pixel of each image in a three-dimensional space. However, such an algorithm only operates if the illumination source is a point light source and if directions of illumination sources are known. In the domain of biological analysis, for example, the illumination sources are not point light sources and the location of the illumination sources is either unknown or not easy to model. Thus, the algorithm cannot be applied to such images. In addition, the optical properties or orientation of the surface of biological objects are not known. Thus, albedo values for such objects are not known. Furthermore, no parameters related to the intensity of the illumination sources are taken into account in this algorithm. Thus, during the reconstruction process, information regarding the properties of the surface of the object may be lacking or insufficient and lead to incorrect representation of the surface of the object. As a result, such an algorithm is not always suitable for use in photometric stereo reconstruction of images taken in the domain of biological analysis or of any images taken with a fixed position camera and complex illumination conditions or unknown illumination directions.
The bibliographical reference [13] discloses an improved algorithm for use in a photometric stereo method. The algorithm takes into account a fourth matrix related to value L which represents the various intensities of the illumination sources. However, such an algorithm only operates if the positions of illumination sources and/or normals of specific pixels of images are known. In some situations, such information is either not available or difficult to obtain. Thus, approximations are necessary to apply this algorithm. As a result, such an algorithm is not suitable for generating an accurate image of a surface of an object if illumination conditions are unknown, as is often the case in the domain of biological analysis. In addition, approximations may lead to incorrect representation of the image of the surface of the object at the end of the image reconstruction process.
As a result, there is a need to improve the standard photometric stereo method to allow the generation of a raised relief map of the surface of an object to enable the reconstruction of a surface of an object for which no prior information is available and which are associated with a plurality of illumination sources with different illumination directions.